The present invention relates to video (digital) images. Each digital image comprises a plurality (typically hundreds or thousands) of pixels. Each pixel contains information (values) about the chrominance and luminance of a small portion of the image. The present invention relates to a technique for analyzing the individual pixels of a digital image to determine whether the pixel has “noise”, then correcting the pixel values to reduce the noise level of the pixel. This is done by “filtering” the pixels of the images.
Correcting or reducing the noise level of image pixels is important for at least two reasons. First, the resulting image can (should) look better (fewer defects). Second, modern digital compression techniques function by detecting changes in images, such as motion, and can benefit from images that have less noise. The overall goal of effective filtering is to reduce abrupt changes, without sacrificing picture quality (sharpness).
A problem which is pervasive to noise filtering is that certain features of images, such as object edges, can look a lot like noise when filtering is performed on a pixel-by-pixel basis. An effective noise-filtering algorithm is one which can reduce noise without sacrificing picture quality. Generally speaking, there are two types of image filtering, “spatial” and “temporal”.
In spatial filtering, the value of a given pixel is compared to the values of the pixels surrounding (in the vicinity of, in the neighborhood of) the given pixel, in a given image. The given pixel may, for example, be a center pixel of a 3×3 array of pixels. If, for example, the center pixel in the array had a value of 1000*X, and the surrounding pixels all had a value of 2*X, it could be assumed that the value of the center pixel is erroneous, and it could be altered, using a number of techniques, to bring it into line with the values of the surrounding pixels. In temporal filtering, the values of a given pixel at a specific location within an image is compared with the values for a pixel at the same location in a previous or subsequent image. If the value of a pixel at a certain location within a given image varies dramatically from the value of pixels at the same location within a previous and/or subsequent image, its value can be modified to bring it more into line with what would be expected. Combinations of spatial and temporal filtering are also known.
Noise reduction of a video signal is used to enhance the quality of images comprising the video signal and to prepare for an efficient compression of the video signal. Noise reduction is important in connection with compression of image information, because noise may significantly reduce the effectiveness of compression schemes, particularly frequency-domain compression schemes such as the various MPEG video standards. In image compression technology there is typically a trade-off between compression and image quality; increased compression may tend to reduce image quality. It is not always easy to reconcile these differences so as to achieve high quality highly compressed images. Effective noise reduction in connection with compression of a video signal may well serve both purposes and produce enhanced images in addition to a well-compressed video signal.
Video compression lowers the necessary bandwidth for transmitting moving pictures by removing picture redundancy. This is done in both spatial and temporal domains.
The process begins with a conversion from spatial to frequency domain via a Discrete Cosine Transform (DCT). This transform works on square groups of pixels (termed “blocks”). Having transformed the picture from the spatial domain, the bandwidth may be further lowered by use of clever coding schemes such as variable-length (VLC) and run-length coding (RLC).
Noise in video arises from a variety of sources. Most basic is the wideband or Gaussian noise that comes from pickup devices (camera tubes or CCDs), film grain, analog circuits, and so forth. For signals that have been transmitted over analog links, it is also common to see impulse noise. This type of noise is especially common in satellite and microwave links (and may range in intensity from a “sparkle” or two a minute to the “waterfall” of impulses seen in a satellite feed about to go into solar outage), but impulses may also come from inside a facility (the custodian plugging a vacuum cleaner into technical power, for example). A final class of noise, which is termed “surface impairments” comes from, for example, vertical scratches present on film-stock that has been mistreated. Noise in this class may also come from signal cross talk. Once the noise is present in the signal it is very difficult to remove. Historically, techniques such as high-frequency roll-off have been employed, frequently doing more damage to the underlying pictures than the noise itself.
The filters available for reducing noise include both temporal and spatial filters (the vertical filters required external hardware). The present invention applies to spatial filtering. Basic spatial filtering, which applies horizontal and vertical low-pass filtering within a frame, discards both noise and picture detail. This technique can offer a trade off between artifacts and softer pictures, however the effect of soft pictures is easily seen. U.S. Pat. No. 6,229,578 ('578 Patent) discloses an edge-detection based noise removal algorithm. What is disclosed is a method for removing noise by distinguishing between edge and non-edge pixels and applying a first noise removal technique to pixels classified as non-edge pixels and a second noise removal technique to pixels classified as edge pixels. The methodology operates on images while in a Color Filter Array (CFA) domain prior to color interpolation, and uses techniques suited to the classification, whether edge or non-edge.
As discussed in the '578 Patent, in the art of image processing, raw images of an object/scene captured from a sensing or capture device are often subject to varying types of “noise” (elements not present in the object or environment which may nonetheless appear in the image). The noise present in an image may be due to the characteristics of the imaging system such as the sensor or processing steps subsequent to the initial image capture which may add noise while trying to achieve a different purpose. The properties and characteristics that would indicate that a pixel or region of pixels is “noisy” and the properties that would indicate a pixel or region of pixels is an edge or a fine detail of the image are difficult to distinguish. Thus, a fundamental problem with the removal of noise is that often a removal of what is indicated as noise may actually be a removal of fine edge or detail. If the fine detail or edge is removed, a blurring effect may occur within that region of the image further, in color images, the blurring effect leads to a bleeding of one color across the edge to another pixel(s). Noise removal procedures that were based upon linear filtering techniques suffered greatly from this malady and thus, a class of filtering techniques based on ranked order statistics such as the median filter were developed.
As discussed in the '578 Patent, the median filter ranks in order the intensity values belonging to a pixel P (for which the filter is being applied) and pixels in a particular neighborhood or along a particular vector about a pixel P. For example, a median filter (applied in a particular direction(s) through the pixel to neighboring pixels) applied to sample values including and about the pixel P of {12, 13, 200, 50, 14} would first be ranked in order as {12, 13, 14, 118, 200}. The so-called uni-directional finite impulse response (FIR) median hybrid filter would replace the original pixel location P that had a value of 200 with the median of the sample set which is 14. Thus, the output vector, after the filter, would be: {12, 13, 14, 50, 14}. If the value 200 were in fact part of an edge rather than noise, the smoothing caused by applying the filter as shown in the output vector values would decimate the edge feature.
As discussed in the '578 Patent, several improved median filters have been developed to compensate for this problem. One particular such median filter, the multilevel FIR median hybrid filter repeatedly takes the median filter in each direction about an image and applies at each filter the original input pixel. The multi-level median hybrid filter has averaging sub-filters that reduce the burden of sorting operations by averaging pixels in a particular filter direction, and then performing the median computation upon a smaller set of values, such as three. Thus, in a median hybrid filter, two neighboring west pixels would be averaged and the result fed to a median filter along with the average of two neighboring east pixels. The third input to the median filter is the pixel under consideration for noise removal. In other directions, a similar procedure is applied. In a three-level median hybrid filter, the first level pairs all such averaged neighboring pixels with vectors in opposing directions (north with south, etc.) and for each pair of direction averages (8 of them) feeds these into a median filter also along with the pixel of concern as a third input. The resulting median values of the first filter are again paired and along with the pixel of concern are input to a median filter. While median hybrid has been shown to work quite well in discriminating some edges, it is deficient in several respects with regard to edge detection. The median hybrid filter does not consider the noisiness of the edge itself. In other words, an edge's direction, even though eight are employed, cannot be determined with exacting accuracy. For instance, an edge feature may lie at a 33 degree vector from a particular pixel, and thus the eight directions are inadequate in determining the edge feature. In other words, a single pixel may contain a portion that is edge and a portion that is non-edge in the non-discrete world that cannot be represented in the discrete world of digital images. When applied to digital images, the median hybrid filter, if applied everywhere to all pixels, may propagate noise or shift it from pixel to pixel while attempting to remove it since there is noise along the edge feature due to the non-cardinal direction of the edge. A curved edge is a perfect example of such a problem.
U.S. Pat. No. 5,844,627 ('627 Patent) discloses structure and a method for reducing spatial noise. A digital filter for noise reduction selects between local variances obtained from adjacent pixels in the same frame and adjacent pixels in the same field. In one embodiment, the digital filter includes a filter modified from an adaptive Wiener filter which preserves edges and smoothes smooth areas of the image. A high compression ratio can be achieved in very smooth regions of the image without introducing artifacts.
As discussed in the '627 Patent, video noise reduction filters are often provided for removing artifacts (“noise”) from a video image which are visible to a human viewer. The objective of noise removal is to create a visually pleasing image. Such noise-reduction filters include median filters and linear low-pass filters. Median filters often introduce additional artifacts which corrupt edges in the image. Linear low-pass filters often blur edges in the image. In general, these techniques are provided to remove visible defects from the image so that, for that purpose, introduction of such additional artifacts generally invisible to the eye is tolerated. However, in video signal processing, these “invisible” artifacts can be detrimental to other objectives, such as achieving a high compression ratio for storage and transmission of the video image. A lower compression ratio requires the video processing system to operate at either a higher bit rate (in a variable bit rate encoding application) or a lower image quality (in a fixed bit rate encoding application).
As discussed in the '627 Patent, in the prior art, the Wiener filter and its adaptive field/frame variants are noise-reduction digital filters which have been extensively studied. For example, some local Wiener filters are described in (i) “Digital Image Enhancement and Noise Filtering by Use of Local Statistics” by J. S. Lee, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, No 2, March 1985, pp. 165-168; (ii) “Refined Filtering of Image Noise Using Local Statistics”, J. S. Lee, Computer Graphics and Image Processing 15, 380-389 (1981); and (iii) “Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise”, Kuan et al, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-7, No. 2, March 1985, pp. 165-177. Specifically, local Wiener filters operate on each pixel of an image based on the mean value and the variance value of a finite number of pixels in the immediate vicinity of that pixel. Wiener filters are important in video compression processing for two reasons. First, Wiener filters remove noise that is not very visible to the eye, such as noise related to film grain. Second, as compared to the noise-reduction filters discussed above (e.g. the median filter), a Wiener filter is less prone to introduce new defects, especially those visible to the eye. Thus, Wiener filters are often used to improve compression efficiency.
FIG. 1A, corresponding to FIG. 3a of the '627 patent, shows a pixel's 3×3 neighborhood formed by pixels in the same frame, according to the prior art. The pixel to be filtered is shown in fine cross-hatching, centered among its eight neighbors in the neighborhood, which are shown in coarse cross-hatching.
FIG. 1B, corresponding to FIG. 3b of the '627 patent, shows a pixel's 3×3 neighborhood formed by pixels of the same field, according to the prior art. The pixel to be filtered is shown in fine cross-hatching, centered among its eight neighbors in the neighborhood, which are shown in coarse cross-hatching.
FIG. 1A shows the nine pixels in the 3×3 neighborhood. A neighborhood mean (“field-based mean”) and a neighborhood variance (“field-based variance”) are computed for each pixel, based on averaging and computing the variance of the nine pixels of the same field in the pixel's 3×3 pixel neighborhood.
FIG. 1B shows the nine pixels in the 3×3 neighborhood. For each pixel g(i,j) in the frame, the smaller of the frame-based and field-based neighborhood variances, and its associated neighborhood mean, are chosen to be the neighborhood variance (designated σg2(i,j)) and neighborhood mean (designated bar-g(i,j)), respectively, for that pixel. Independently, the frame-based and field-based neighborhood variances obtained are summed and accumulated for the entire image. The resulting value is used to compute a noise variance (designated σn2) for a global noise signal. Various ways are disclosed for computing σn2.
U.S. Pat. No. 6,335,990 ('990 Patent) discloses a system and method for spatial temporal-filtering for improving compressed digital video. A filter that filters in the spatial and temporal domain in a single step with filtering coefficients that can be varied depending upon the complexity of the video and the motion between the adjacent frames comprises: a IIR filter, a threshold unit, and a coefficient register. The IIR filter and threshold unit are coupled to receive video data. The IIR filter is also coupled to the coefficient register and the threshold unit. The IIR filter receives coefficients, a, from the coefficient register and uses them to filter the video data received. The IIR filter filters the data in the vertical, horizontal and temporal dimensions in a single step. The filtered data output by the IIR filter is sent to the threshold unit. The threshold unit compares the absolute value of the difference between the filtered data and the raw video data to a threshold value from the coefficient register, and then outputs either the raw video data or the filtered data.
FIG. 2, corresponding to FIG. 1 of the '990 patent, is a block diagram of a video processing system including a filter used as a pre-filter, according to the prior art. This video processing system illustrates use of a filter as a pre-filter. As shown, a stream of raw video data is received at the input to the pre-filter. The pre-filter processes and filters the data, and outputs the filtered data. The output of the pre-filter is coupled to a compression unit which compresses the filtered video data and outputs the compressed data to a decompression unit. While the coupling between the compression unit and the decompression unit is shown as an electrical coupling, those skilled in the art will realize that the transmission of the compressed data may take a variety of formats including transfer across a LAN, transfer across the ISDN, transfer across the ATM, transfer across the Internet, transfer through the satellite, transfer through the cable TV or transfer to and from a floppy disk, CD-ROM or other similar suitable medium. The compressed data is provided on the input of the decompression unit. The decompression unit in turn decompresses the data to recreate the filtered video data that is then sent to the display device for presentation to the user. As used in the context of FIG. 2, the pre-filter is preferably provided with coefficients such that the filtered bit stream output after having been compressed and decompressed has a substantially better display quality as compared to bit streams that have not been filtered.
U.S. Pat. No. 6,295,382 ('382 Patent) discloses method and apparatus for establishing an adaptive noise reduction filter. The method and apparatus for adaptive noise filtering within a video graphics circuit includes determining an average intensity for a kernel of a display area. The kernel includes a plurality of pixels arranged in a particular manner, for example a square, a rectangle, etc. Next, a variance for a pixel within the kernel is determined. Based on a relationship between the average intensity and the variance, a signal-to-noise factor is determined. The signal-to-noise factor includes a noise region, a signal region, and an edge region. The pixel within the kernel is then adaptively filtered based on the signal-to-noise factor, the average intensity, and intensity of the pixel.
As discussed in the '382 Patent, an adaptive filter filters noise based on the equation:Yout=K*Xc+(1-K)μ, where K=Sigma2/(Sigma2i+Sigma2n).
In this equation, μ. represents the average pixel value (color, texture, alpha blending, etc.) of the pixels covered by the filter, Sigma.sup. 2 represents the variance within the display screen, Sigma2/Sigma2i represents the local variance, and Sigma2n represents the noise floor. Further,μ=(l/L * W) ΣiΣjXij, where W represents the width (with respect to the x-direction) of the filter and L represents the length (with respect to the y-direction) of the filter. For example, a 3×3 filter encompassing 9 pixels where L equals 3 and W equals 3 wherein Xc represents the center pixel of the filter.
As discussed in the '382 Patent, when this filter is applied to an input video signal, it attenuates noise based on the adaptive filtering equation. In general, when there is significant noise, the K term approaches 1, thus the filter filters Xc based primarily on the pixel value of Xc (i.e., the K*Xc term dominates). When there is little noise, the K term approaches 0, thus the filter filters Xc based on the average pixel value of the pixels covered by the filter (i.e., the (1-K) μ term dominates). When the noise level is in between, both terms contribute to the filtering of the pixel providing additional filtering when it is not needed. As a result, images appear smoother than intended because definition of the edges of the images has been diluted. As such, the desired video quality is less than optimal.
One of the best known and most widely used video compression standards for encoding moving picture images (video) and associated audio is the MPEG-2 standard, provided by the Moving Picture Experts Group (MPEG), a working group of the ISO/IEC (International Organization for Standardization/International Engineering Consortium) in charge of the development of international standards for compression, decompression, processing, and coded representation of moving pictures, audio and their combination. The ISO has offices at 1 rue de Varembé, Case postale 56, CH-1211 Geneva 20, Switzerland. The IEC has offices at 549 West Randolph Street, Suite 600, Chicago, Ill. 60661-2208 USA.
The international standard ISO/IEC 13818-2 “Generic Coding of Moving Pictures and Associated Audio Information: Video”, and ATSC document A/54 “Guide to the Use of the ATSC Digital Television Standard” describes the MPEG-2 encoding scheme for encoding and decoding digital video (and audio) data. The MPEG-2 standard allows for the encoding of video over a wide range of resolutions, including higher resolutions commonly known as HDTV (high definition TV).
In MPEG-2, encoded pictures are made up of pixels. Each 8×8 array of pixels is known as a “block.” A 2×2 array of blocks is referred to as a “macroblock.” MPEG-2 video compression is achieved using a variety of well known techniques, including prediction (motion estimation in the encoder, motion compensation in the decoder), 2-dimensional discrete cosine transformation (DCT) of 8×8 blocks of pixels, quantization of DCT coefficients, and Huffman and run-length coding. Reference frame images, called “I-frames” are encoded without prediction. Predictively-coded frames known as “P-frames” are encoded as a set of predictive parameters relative to previous I-frames or previous P-frames. Bi-directionally predictive coded frames known as “B-frames” are encoded as predictive parameters relative to both previous and subsequent I-frames and P-frames.
The MPEG-2 standard specifies formatting for the various component parts of a multimedia program. Such a program might include, for example, MPEG-2 compressed video, compressed audio, control data and/or user data. The standard also defines how these component parts are combined into a single synchronous bit stream. The process of combining the components into a single stream is known as multiplexing. The multiplexed stream may be transmitted over any of a variety of links, such as Radio Frequency Links (UHF/VHF), Digital Broadcast Satellite Links, Cable TV Networks, Standard Terrestrial Communication Links, Microwave Line of Sight (LoS) Links (wireless), Digital Subscriber Links (ADSL family), Packet/Cell Links (ATM, IP, IPv6, Ethernet).
U.S. Pat. No. 5,974,193 ('193 Patent) discloses a technique for noise reduction in association with MPEG-1 and MPEG-2 encoding of video signals.
As discussed in the '193 Patent, an MPEG transmission system allows several video, audio and associated services to be multiplexed and sent over a single digital transmission channel. The number of services and hence the cost of transmission bandwidth per service is determined by the bit rate. Any improvement in picture quality or reduction in bit rate is thus very important to a service provider.
As explained in the '193 Patent, most sources of video produce random noise: camera noise, tape noise and the digital re-transmission of existing analog services are typical examples of systems introducing noise. Although much of this noise is often biased towards the high frequency parts of the spectrum and is not particularly visible in an analog system, MPEG encoding of such material often introduces Discrete Cosine Transform (DCT) effects or artifacts that “crawl” around the picture.
As also mentioned in the '193 Patent, there are two main reasons for these effects being produced. First, the presence of noise causes many small amplitude high frequency DCT coefficients to be generated and sent in the bit stream. These coefficients tend to be more inaccurately quantized than the low frequency coefficients and are generally due to the noise only. The increase in the number of bits transmitted causes the quantization Parameters factor (QP) to become higher in order to maintain the same bit rate. The net result is that the whole picture is reduced in quality. The Forward Prediction (P) and Bi-directional prediction (B) frames that follow the Intra (I) frame try to constantly correct for the noise in the prediction path and so this results in the DCT artifacts changing from frame to frame. The second reason for the loss in picture quality is that the accuracy of the motion estimation is reduced with the presence of noise in the encoder itself. This produces even worse predictions in the ‘B’, and ‘P’ frames which inevitably increases the QP and reduces picture quality.
A spatio-temporal noise reduction scheme for interlaced video is disclosed in “Perceptive Adaptive Temporal TV-Noise Reduction using Contour Preserving Prefilter Techniques”, K. Jostschulte, A. Amer, M. Schu, H. Schroeder, IEEE Transactions of Consumer Electronics, Vol.44, No.3, pp. 1091-1098, 1998 (“Jostschulte”). The noise reduction scheme consists mainly of a subband based temporal recursive filter which makes use of some special properties of the human visual system. This temporal system is supported by a preceding detail preserving spatial filter with low hardware expense, which consists of an image analyzing high pass filter bank and an adaptive low pass FIR-filter for noise reduction. Both the spatial and temporal noise reduction were evaluated with a large amount of simulations that result in a very good objective and subjective efficiency. Furthermore, the chain of both temporal and spatial noise reduction may even yield results which are better than the sum of pure spatial and temporal techniques.
Jostschulte is geared towards improvement of image quality techniques in consumer television receivers. One of these image quality improvement tasks is noise reduction.
The image can be corrupted by noise in different ways. Some noise sources are located in a camera and become active during image acquisition especially under bad lighting conditions. Here different types of noise are added due to the amplifiers and other physical effects in the camera. Further noise sources take effect due to transmission over analog channels, e.g. satellite or terrestrial broadcasting. Digital transmission inserts other distortions which also may have a noisy characteristic. Further noise is added by image recording devices such as VCRs. In these devices, additive white Gaussian noise or, in the case of tape drop-outs, impulsive noise is added to the signal. Because of this it can be very important in a television receiver to perform a final reduction of all these distortions.
Spatial noise reduction is performed by application of linear or nonlinear operators which use correlations within an image. But a spatial noise reduction only has a subjective and objective gain if edges are preserved. So this filter must be controlled by a special image analyzer which controls the coefficients of such a filter.
In Jostschulte, a complete system of a spatio-temporal noise reduction scheme is presented. Jostschulte aptly notes that the problem of a spatial noise reduction scheme is to eliminate spatially uncorrelated noise from spatially correlated image content. One way of doing this is with a spatial low pass filter. Such a filter can be implemented, e.g., as a horizontal, vertical or diagonal 3-tap FIR-filter as depicted in FIG. 2 of Jostschulte, which is reproduced as FIG. 3 herein. This figure is a block diagram of a simple filter for spatial noise reduction.
FIG. 4, corresponding to FIG. 4 of Jostschulte, is a block diagram of a filter for detail preserving spatial noise reduction. FIG. 5, corresponding to FIG. 5 of Jostschulte, is a diagram illustrating eight masks for filtering a pixel.
As discussed in Jostschulte, the filter is assumed to have input noise variance σ2in. The resulting output variance σ2out of this filter is given in the following equation:σ2out=r2*σ2in+2*((1−r)/2)2*σ2in 
With the assumption that the filter does not influence the original image signal, the noise reduction R (ratio of signal to noise values of input and output) of such type of filter is given by:R[dB]=10*log(σ2in/σ2out)=10*log(2/(3r2−2r+1))
The dependency of the central coefficient and the noise reduction of such a filter is depicted in Jostschulte FIG. 3 (not shown herein). For a simple cos2-shaped filter, a noise reduction value of 4.26 dB results. The maximum is achieved for a mean filter.
As noted in Jostschulte, the disadvantage of such a system is the tendency to blur edges and lines of the image. For this reason, a spatial noise reduction has to be adaptive to the spatial image content. In other words, a spatial filter only has to be applied along object boundaries or in unstructured areas. As a result, an image analyzing step has to be applied which controls the direction of the low pass filter. This is shown in FIG. 4 (corresponding to FIG. 4 of Jostschulte).
Jostschulte mentions that several algorithms for precise detection of edge-directions are known, but that nearly all of them have in common that a hardware implementation will be very expensive. In this case, the demand was a system that is easy to implement. So another method of detecting edge-directions was chosen. It consists of a set of high pass filters which are able to detect eight different directions of edges and structures.
FIG. 5 (corresponding to FIG. 5 of Jostschulte) depicts eight different masks for directions of analyzing and filtering. All are based on a 3×3 array of pixels, with the pixel being analyzed/filtered at the center of the array. The pixel in the center of the array is, of course, included in all of the masks. As can be seen, special masks for corners are also considered. According to Jostschulte, if this were not the case, sharpness in object-corners could be lost.
Mask 1 includes the three pixels extending horizontally across the center of the 3×3 array. Mask 2 includes the three pixels extending vertically up the center of the 3×3 array. Mask 3 contains the three pixels extending diagonally, from top left to bottom right, across the array. Mask 4 has the three pixels extending diagonally, from top right to bottom left, across the array. Mask 5 includes the center right pixel and the center bottom pixel, defining a lower right corner. Mask 6 includes the center left pixel and the center bottom pixel, defining a lower left corner. Mask 7 includes the center left pixel and the center top pixel, defining a top left corner. Mask 8 includes the center right pixel and the center top pixel, defining a top right corner.
In the image analyzer, zero-mean high pass filters with coefficients {−¼, ½, −¼} are applied along all given directions for each pixel of the image. The direction with the lowest absolute high pass output is chosen to be the direction of the local picture contour. The result is that the following low pass filter of the same direction will not blur structures in the image.
Simulations concerning the PSNR result in noise reduction values R of 1 dB up to 2 dB. The amount of noise reduction was found to be dependent on the contents of the image. In structured areas, the results were higher than in nearly unstructured areas. The loss in unstructured areas can be explained with the tendency of the analyzing filter to fail in noise structures. In such cases, the mask selection is not uncorrelated to noise. That is the reason why theoretical values of R are higher than simulated ones. But even in completely unstructured images, the noise reduction is about 1 dB.
Pre-processing video to reduce entropy by attenuating spatial noise allows a trade-off between compression and smoothing artifacts. Locally adaptable directional low pass filtering as described in Jostschulte minimizes edge blurring, but provides no means to adjust the degree of smoothing for optimized quality at a given bit rate. It would be advantageous to provide a system and methods for adjusting the degree of smoothing, thereby optimizing quality.
The present invention provides directional video filtering techniques for locally adaptive spatial noise reduction having the aforementioned and other advantages.